AI-Readiness Check

Which of these best describes your role?

Run/manage data pipelines

Manage any type of data team

C-suite or Vice President

Please select a role to continue.

How effective is your data processing solution in terms of architecture, scalability, security, and usability, considering platform scope and diverse data handling?

Not effective

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Very effective

Not effective

Very effective

Please select a response.

Do you find it easy to get hold of all the source data you need (e.g. APIs, databases, structured, unstructured and semi-structured data, SaaS, and on-prem)?

Not at all

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Very easily

Not at all

Very easily

Please select a response.

What proportion of your data pipelines are hand-coded?

All of them

100% 100%

90% 90%

80% 80%

70% 70%

60% 60%

50% 50%

40% 40%

30% 30%

20% 20%

10% 10%

Hardly any

All of them

Hardly any

Please select a response.

Are your data pipelines consistently and accurately annotated and documented?

Very inconsistently

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Consistently

Very inconsistently

Consistently

Please select a response.

How scalable is your data architecture in terms of its ability to support any number of ongoing initiatives, and how well does it dynamically scale up or down to ensure optimal performance?

Not at all

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Completely

Not at all

Completely

Please select a response.

How easy is it in your organization to onboard new data engineers and for people of all technical backgrounds to self-serve with data?

Not possible, due to high technical barriers

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Our infrastructure makes it easy

Not possible, due to high technical barriers

Our infrastructure makes it easy

Please select a response.

When integrating data from multiple different sources, to what level is context switching between platforms an issue?

Difficult to orchestrate as multiple tools are needed

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Very easy

Difficult to orchestrate as multiple tools are needed

Very easy

Please select a response.

How effective are your Change Data Capture (CDC) processes?

Ineffective and difficult

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Reliable and highly maintainable

Ineffective and difficult

Reliable and highly maintainable

Please select a response.

To what degree are you able to include Gen AI data transformations in your data-engineering (e.g. sentiment analysis, categorization, translation, summarization etc)

Not at all

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Very easily

Not at all

Very easily

Please select a response.

How efficient is your present data processing solution in terms of its architecture, efficiency, flexibility and scalability? (Consider the skills needed to operate it, and how easily it handles diverse data types)

Not efficient

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Very efficient

Not efficient

Very efficient

Please select a response.

Does your current infrastructure allow you to easily get hold of all the source data you need (e.g, APIs, databases, structured, unstructured and semi-structured data, SaaS, and on-prem)?

Not at all

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Very easily

Not at all

Very easily

Please select a response.

To what degree does your current solution enable you to meet your compliance and governance controls?

Not at all

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Very easily

Not at all

Very easily

Please select a response.

A large portion of business data is unstructured – Slack threads, call logs, emails, etc. Does your current solution allow you to handle this kind of data?

Not at all

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Completely

Not at all

Completely

Please select a response.

To what extent does your current solution allow you to create sophisticated and parameterizable data transformation and integration processes?

Not at all

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Completely

Not at all

Completely

Please select a response.

Do all members of your data team find it easy to generate insights from many different kinds of data, while avoiding repetition and maintaining compliance?

With great difficulty

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Very easily with metadata management

With great difficulty

Very easily with metadata management

Please select a response.

To what scale can your platform easily adapt to the largest data volumes?

With great difficulty

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Very easily

With great difficulty

Very easily

Please select a response.

How well does your current system allow you to combine different data sources securely?

With great difficulty

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Very easily

With great difficulty

Very easily

Please select a response.

How effective is your current system at ensuring your machine learning models are using the most current data, for best prediction accuracy?

It's difficult, and not effective

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Easy and effective

It's difficult, and not effective

Easy and effective

Please select a response.

Consider the time-to-value in cases when you need to securely combine and interpret structured, semi-structured and unstructured data. How cost effective is your present data processing solution?

Not cost-effective

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Very cost-effective

Not cost-effective

Very cost-effective

Please select a response.

Does your data team struggle with costly and inefficient tool switching in order to perform full data integration?

Yes, it's an efficiency problem

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No problem

Yes, it's an efficiency problem

No problem

Please select a response.

Is your data team able to efficiently combine varied data transformation techniques (such as dbt core, Python, Spark) with unlimited source connectivity and unstructured data handling?

With great difficulty

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Very easily

With great difficulty

Very easily

Please select a response.

Does the user interface of your data processing solution help your team's new (and especially less experienced members) with quick onboarding and easy data handling?

Significant training needed

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Highly productive immediately

Significant training needed

Highly productive immediately

Please select a response.

Do you find your data processing solution's pricing model optimal for collaboration, easy scaling, and for supporting many data sources?

Limiting and/or problematic

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Highly suitable

Limiting and/or problematic

Highly suitable

Please select a response.

Does your team spend a lot of time on manual and technical tuning activities such as scaling and distributing workloads?

Significant time spent manually tuning and managing workloads

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We have automated most tuning and scaling tasks

Significant time spent manually tuning and managing workloads

We have automated most tuning and scaling tasks

Please select a response.

Does your current data processing solution automatically reduce unnecessary infrastructure expenses by adjusting resource allocation based on demand?

With great difficulty

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Very easily with dynamic resource allocation

With great difficulty

Very easily with dynamic resource allocation

Please select a response.

How easy do you find it to be guaranteed that your data processing solution meets data governance standards and complies with regulations like GDPR, CCPA, and HIPAA?

Very difficult

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Very easy

Very difficult

Very easy

Please select a response.

How much of the TCO of your data processing solution comes just from the staffing cost of your dedicated data team?

Almost all: sophisticated data processing needs highly experienced data engineers

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Almost none: our data processing solution can be used by anyone

Almost all: sophisticated data processing needs highly experienced data engineers

Almost none: our data processing solution can be used by anyone

Please select a response.

Your AI-Readiness score

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